Statistical Relational Learning to Recognise Textual Entailment

We propose a novel approach to recognise textual entailment RTE following a two-stage architecture --- alignment and decision --- where both stages are based on semantic representations. In the alignment stage the entailment candidate pairs are represented and aligned using predicate-argument structures. In the decision stage, a Markov Logic Network MLN is learnt using rich relational information from the alignment stage to predict an entailment decision. We evaluate this approach using the RTE Challenge datasets. It achieves the best results for the RTE-3 dataset and shows comparable performance against the state of the art approaches for other datasets.